Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on Lq-distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor ...

We introduce a new nearest neighbor search algorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point, performs hill-climbing starting from a randomly sampled node of the graph. We provide theoretical guarantees for the accuracy and the computational complexity and empirically show the effectiveness of this algorithm.

Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to fi...

This paper proposes a rotational invariant texture feature based on the roughness property of the image for psoriasis image analysis. In this work, we have applied this feature for image classification and segmentation. The fuzzy concept is employed to overcome the imprecision of roughness. Since the psoriasis lesion is modeled by a rough surface, the feature is extended for calculating the Pso...

Conventionally, the k nearest-neighbor (kNN) classification is implemented with use of Euclidean distance-based measures, which are mainly one-to-one similarity relationships such as to lose connections between different samples. As a strategy alleviate this issue, coefficients coded by sparse representation have played role gauger for well. Although SR enjoy remarkable discrimination nature on...

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